https://github.com/brijeshrakhasiya/trip-planner-agent
AI-powered travel planner built with CrewAI, Streamlit, and Ollama LLM. Generates personalized itineraries using multi-agent collaboration, real-time web search, and a user-friendly interface.
https://github.com/brijeshrakhasiya/trip-planner-agent
agentic-ai crewai crewai-tools langchain multi-agent-system personalized-travel trip-planning
Last synced: about 1 month ago
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AI-powered travel planner built with CrewAI, Streamlit, and Ollama LLM. Generates personalized itineraries using multi-agent collaboration, real-time web search, and a user-friendly interface.
- Host: GitHub
- URL: https://github.com/brijeshrakhasiya/trip-planner-agent
- Owner: BrijeshRakhasiya
- License: mit
- Created: 2025-10-02T07:57:40.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-02T08:07:47.000Z (9 months ago)
- Last Synced: 2025-10-02T09:29:53.506Z (9 months ago)
- Topics: agentic-ai, crewai, crewai-tools, langchain, multi-agent-system, personalized-travel, trip-planning
- Language: Python
- Homepage:
- Size: 272 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ✈️ AI-Powered Trip Planner
An intelligent travel planning app built with **CrewAI**, **Streamlit**, and **Ollama LLM**, designed to generate personalized travel itineraries based on user preferences.
---
## 🚀 Features
- 🧠 **AI-Powered Planning**: Multi-agent system (Location, Guide, Planner Experts) for holistic travel insights
- 🗺️ **Comprehensive Itineraries**: Covers accommodations, transport, food, events, and budgeting
- 🌐 **Live Web Search**: DuckDuckGo integration for real-time travel data
- 🖥️ **Streamlit Interface**: Intuitive UI for entering travel details
- 📄 **Downloadable Plans**: Export itineraries as text files
- 🌍 **Multi-language Support**: French responses for Francophone destinations
---
## 🧩 Architecture
Three specialized AI agents collaborate to deliver a complete travel plan:
| Agent | Role |
|------------------|----------------------------------------------------------------------|
| 🏨 Location Expert | Manages logistics: visas, weather, transport, accommodation, costs |
| 🎯 Guide Expert | Recommends attractions, food, and activities based on interests |
| 📅 Planner Expert | Compiles all data into a structured, day-by-day itinerary |
---
## ⚙️ Installation
### 📋 Prerequisites
- Python 3.8+
- Ollama installed and running locally
- Llama 3.2 model pulled via:
```bash
ollama pull llama3.2
### Setup
- Clone the repo
```
git clone https://github.com/BrijeshRakhasiya/Trip-Planner-Agent.git
```
- Install dependencies
```
pip install -r requirements.txt
```
- Start Ollama
```
ollama serve
ollama pull llama3.2
```
## Usage
1. Run the Streamlit application:
```bash
streamlit run app.py
```
2. Open your browser to the provided local URL (typically http://localhost:8501)
3. Fill in the travel details:
- From City
- Destination City
- Departure Date
- Return Date
- Interests (e.g., sightseeing, food, adventure)
4. Click "Generate Travel Plan" and wait for the AI to create your personalized itinerary
5. Download the travel plan as a text file
## Dependencies
- `crewai`: Multi-agent AI framework
- `crewai_tools`: Additional tools for CrewAI
- `langchain`: LLM framework integration
- `langchain_community`: Community tools for LangChain
- `langchain-ollama`: Ollama integration for LangChain
- `duckduckgo-search`: Web search functionality
- `langchain-google-genai`: Google Generative AI integration (optional)
- `streamlit`: Web application framework
## Project Structure
```
├── app.py # Main Streamlit application
├── TravelAgents.py # AI agent definitions
├── TravelTasks.py # Task definitions for agents
├── TravelTools.py # Custom tools (web search)
├── requirements.txt # Python dependencies
├── output/ # Generated travel plans
│ └── Travel_Plan_Rome.txt # Sample output
├── git_assets/ # UI screenshots
│ ├── 1.png
│ ├── 2.png
│ └── 3.png
└── __pycache__/ # Python bytecode cache
```
## Screenshots
### Main Interface
### Travel Plan Generation
### Sample Output
## Sample Output
See `output/Travel_Plan_Rome.txt` for a sample travel plan generated for Rome, focusing on accommodation recommendations.
## Technical Details
- **LLM**: Uses Ollama with Llama 3.2 model running locally
- **Process**: Sequential agent execution for comprehensive planning
- **Tools**: DuckDuckGo web search for real-time information
- **Output Format**: Markdown-structured travel itineraries
- **Language**: Python 3.x with async capabilities
## Configuration
The application uses the following configurations:
- Max iterations per agent: 5
- Verbose logging: Enabled
- Full output: Enabled
- Delegation: Disabled (agents work independently)
## 🧯 Troubleshooting
- Ensure Ollama is running before starting the application
- Check that the Llama 3.2 model is downloaded
- Verify all dependencies are installed
- 🌐 For web search issues, ensure internet connectivity
## 🌟 Future Enhancements
- Support for multiple LLMs
- Integration with booking APIs
- Multi-language interface
- Real-time flight/hotel pricing
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙋♂️ Author
**Brijesh Rakhasiya**
AI/ML Engineer · Data Scientist · Problem Solver
---
**👨💻 Developed by Brijesh Rakhasiya**